Minimize function with Pandas dataframe

Question

I have to find the 2 input values for which the output value is minimized:

import pandas as pd

def calc_func(x1, x2):
    var1 =  pd.DataFrame([x1]).loc[0]
    var2 =  pd.DataFrame([x2]).loc[0] 
    y = var1-var2
    return(y)

from scipy.optimize import minimize
x0 = [1,2,3]
res = minimize(calc_func,x0,x0, method='BFGS')

however this gives me the following error

ValueError: setting an array element with a sequence.

Which can be explained by the fact that the calculations with pandas dataframes use single numbers instead of arrays...

What is the best way to minimize my function?

Comments:

Unfortunately it is not an option to completeley remove all pandas calculations from the function.

So basically the error is in the hidden code? How can one minimize it if one does not know how it looks like? — Quang Hoang
– Quang Hoang, Commented Apr 30, 2019 at 11:46
I changed the hidden code with some fake code. However, in reality the code is more complex, and makes sense to minimize. I hope the fake code will clarify my question. — Koot6133
– Koot6133, Commented Apr 30, 2019 at 11:56
According to the documentation docs.scipy.org/doc/scipy/reference/generated/… fucntion calc_func should return a single float value. Right now calc_func returns pandas series which is a collection of many values. — user10325516
– user10325516, Commented Apr 30, 2019 at 12:14
@Poolka: Not exactly right, it is true that the object is pandas series, but it's not many values. He needs to return a float though, which is easily fixed but not the source of the problem — jDoe
– jDoe, Commented Apr 30, 2019 at 12:22

jDoe · Accepted Answer · 2019-04-30 12:43:35Z

1

The function minimize minimizes a function from R^n to R. The simplest thing to do, is to have x,y both concatenated in a single vector z, then optimize the function with respect to z. The following code works:

import pandas as pd
from scipy.optimize import minimize
import numpy as np

def calc_func(x):
    return(x[0]-x[1])

x1 = [1,2,3]
x2 = [3,4,5]
v1 = pd.DataFrame([x1]).values[0]
v2 = pd.DataFrame([x2]).values[0]
x = np.array([v1,v2])
res = minimize(calc_func,x, method='BFGS')

If you really need to optimize a function from R^n to R^m then you need to use another method (which I did not find by quickly looking in the docs).

edited Apr 30, 2019 at 12:43

answered Apr 30, 2019 at 12:35

jDoe

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